Published online 04 September 2014
Nucleic Acids Research, 2014, Vol. 42, No. 17 10987–10999
doi: 10.1093/nar/gku790
Control of transcription elongation by GreA
determines rate of gene expression in Streptococcus
pneumoniae
Yulia Yuzenkova1,† , Pamela Gamba2,† , Martijn Herber2 , Laetitia Attaiech2 , Sulman Shafeeq2 ,
Oscar P. Kuipers2 , Stefan Klumpp3,* , Nikolay Zenkin1,* and Jan-Willem Veening2,*
1
Centre for Bacterial Cell Biology, Institute for Cell and Molecular Biosciences, Newcastle University, Richardson
Road, Newcastle upon Tyne, NE2 4AX, UK, 2 Molecular Genetics Group, Groningen Biomolecular Sciences and
Biotechnology Institute, Centre for Synthetic Biology, University of Groningen, Nijenborgh 7, 9747 AG, Groningen,
The Netherlands and 3 Max Planck Institute of Colloids and Interfaces, Science Park Golm, 14424 Potsdam, Germany
Received October 29, 2013; Revised August 18, 2014; Accepted August 20, 2014
ABSTRACT
Transcription by RNA polymerase may be interrupted
by pauses caused by backtracking or misincorporation that can be resolved by the conserved bacterial Gre-factors. However, the consequences of such
pausing in the living cell remain obscure. Here, we
developed molecular biology and transcriptome sequencing tools in the human pathogen Streptococcus pneumoniae and provide evidence that transcription elongation is rate-limiting on highly expressed
genes. Our results suggest that transcription elongation may be a highly regulated step of gene expression in S. pneumoniae. Regulation is accomplished
via long-living elongation pauses and their resolution by elongation factor GreA. Interestingly, mathematical modeling indicates that long-living pauses
cause queuing of RNA polymerases, which results
in ‘transcription traffic jams’ on the gene and thus
blocks its expression. Together, our results suggest
that long-living pauses and RNA polymerase queues
caused by them are a major problem on highly expressed genes and are detrimental for cell viability.
The major and possibly sole function of GreA in S.
pneumoniae is to prevent formation of backtracked
elongation complexes.
INTRODUCTION
Transcription, the first step of gene expression, is accomplished by highly conserved multisubunit RNA polymerases (RNAPs). Though initiation is the most heavily
regulated step of the transcription cycle, accurate and processive elongation of RNA is essential for cell viability and
homeostasis. Elongation processivity can be disrupted by
pauses including backtracked pauses when the 3 end of
RNA disengages from the active center and RNAP shifts
backwards (1). Backtracking is also caused by misincorporation events (2–5). Backtracked complexes can be resolved
by hydrolysis of the phosphodiester bond of RNA that reestablishes the 3 end of RNA in the active center allowing its further elongation. Based on in vitro experiments it
was shown that RNA hydrolysis by the RNAP active center
might contribute to overall fidelity and processivity (2–5).
Intrinsic cleavage activity of the RNAP active center can
be greatly stimulated by the evolutionary conserved transcription factor Gre (some bacteria have two factors, GreA
and GreB) (3,6). Gre-factor has a long coiled-coil domain,
which can bind in the secondary channel toward the RNAP
catalytic center. Two conserved acidic residues on the tip
of this domain, D41 and E44 (Escherichia coli numbering),
are thought to stabilize the second catalytic Mg2+ ion in
RNAPs active center and possibly coordinate the attacking
water molecule (3,6–9). Gre was shown to suppress transcription pauses and arrests (10,11), and enhance transcription fidelity in vitro (2,3). A greA deletion in E. coli also
strongly affected the bistable regulation of the lac operon,
which was explained by reduced transcription fidelity in
vivo (12). Overexpression of E. coli GreA (GreAEco ) resulted in upregulation of more than 100 genes (∼2.4% of the
genome) (13). This regulation was proposed to be accomplished through stimulation of transition from transcription
initiation to elongation; i.e. promoter escape. In accordance
with this idea, an increase in the amount of abortive transcripts was demonstrated in the absence of GreAEco (14) and
* To
whom correspondence should be addressed. Tel: +31 50 363 2408; Fax: +31 50 363 2348; Email: [email protected]
Correspondence may also be addressed to Stefan Klumpp. Tel: +49 331 567 9620; Fax: +49 331 567 9612; Email: [email protected]
Correspondence may also be addressed to Nikolay Zenkin. Tel: +44 91 208 3227; Fax: +44 191 208 3205; Email: [email protected]
†
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
C The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
10988 Nucleic Acids Research, 2014, Vol. 42, No. 17
stimulation of promoter escape was suggested to be the major role of Gre in cells in general (13).
Most information on the in vivo function of Gre comes
from studies using E. coli. Interpretation of the effects of
Gre mutants in E. coli are hampered by the presence of two
Gre-factors (GreAEco and GreBEco ) and a number of factors
that potentially can also bind and modulate RNAP through
the secondary channel (e.g. DksA, Rnk and TraR) (15–18).
The functions of these proteins are at least partially redundant. For example, the growth deficiency of a dksA mutant was complemented by multicopy greA and greB (18).
Limited functional studies have been performed with bacteria besides E. coli; a chromatin immunoprecipitation study
showed that Bacillus subtilis GreA is uniformly distributed
over actively transcribed regions and that its inactivation
resulted in the accumulation of RNAP at many promoter
or promoter–proximal regions. However, no change in gene
expression or phenotype was observed (19). Thus, it is clear
that a full functional analysis, both in vitro and in vivo, of a
nonredundant Gre-factor is missing to identify the main in
vivo role of bacterial Gre -factors.
The genome of the Gram-positive human pathogen
Streptococcus pneumoniae only contains a single Gre-factor,
GreASpn , while no homologs of Gre or other known transcription factors that bind to RNAP’s secondary channel
have been identified (20,21). GreASpn contains the two conserved acidic residues present in all Gre-factors that are required for their activity (Figure 1A). A reduced genome of
just 2 million base pairs together with its genetic amenability (22), makes S. pneumoniae an excellent model to study
the physiological role of bacterial Gre-factors. Using transcriptome sequencing (RNA-Seq), newly developed bioinformatics tools, in vitro and in vivo analysis and mathematical modeling we show that the major in vivo function of
GreASpn is to prevent long-living pauses which cause queuing or traffic jams of RNAPs and dramatically hamper gene
expression.
In vitro transcription assays
For transcription from promoters, 0.1 pmole of polymerase
chain reaction (PCR) fragment carrying promoter (obtained with oligonucleotides from Supplementary Table S6)
was mixed with 0.3 pmole of RNAPSpn and 0.9 pmoles of
␴ A with or without 0.1 pmole of GreASpn in transcription
buffer (TB; 33 mM Tris-Ac pH 7.9, 100 mM KGlu, 10 mM
MgAc, 0.5 mM DTT, 0.1 mg/ml bovine serum albumin).
All reactions were performed at 37◦ C. After 10 min of open
complex formation, reactions were started by addition of
0.5 mM ATP, GTP and CTP and 0.15 mM [␣-32 P]-UTP
(7.5 Ci/mmol) final for run-off transcription. For abortive
initiation, assay reactions were started with 50 ␮M dinucleotide primer and 20 ␮M [␣-32 P]-NTP (125 Ci/mmol).
For the promoter of SP 0267, UpA and UTP were used,
for both ccpA and purC, ApA and GTP were used. Reactions were incubated for 10 min and stopped by addition
of formamide-containing buffer. Products were resolved by
denaturing (8 M urea) PAGE and revealed by Phosphorimaging (GE Healthcare).
Elongation complexes were assembled in TB lacking
Mg2+ with 13 nt-long 5 end radiolabeled RNA as described
in (5), except that complexes were immobilized on streptavidin agarose beads (Fluka) through biotin of the 5 end
of the DNA template strand (3). To form misincorporated
complex (mEC14), 10 mM ATP and 20 mM MgCl2 were
added for 30 s. After that, 1 mM NTPs were added in the
presence or absence of 10 nM GreASpn for times indicated
in the figure. Reactions were stopped and products analyzed
as above.
Permanganate footprinting of open complexes was performed on promoters 32 P-labeled on either template or
nontemplate strand, by addition of 5 mM KMnO4 for
30 s, in the presence of 0.5 mM GTP (initiating nucleotide). Reactions were terminated by addition of ␤mercaptoethanol (330 mM), followed by phenol-extraction,
ethanol-precipitation and 10% piperidine treatment.
In vivo measurement of transcription rate
MATERIALS AND METHODS
Strains, plasmids and growth conditions
Bacterial strains and plasmids used in this study are listed in
Supplementary Table S5. The oligonucleotides used in this
study are listed in Supplementary Table S6. Streptococcus
pneumoniae strains were grown as standing cultures in complex C + Y medium (23) at 37◦ C. Detailed growth conditions are described in the supplemental information as well
as the construction of the plasmids and strains used.
Protein purification
Streptococcus pneumoniae RNAP was purified by Polymin
P, heparin and MonoQ chromatographies as described
in (24). Streptococcus pneumoniae ␴ A and GreA (wildtype and mutant) factors were cloned (primers rpoD start
nde/rpoD sp xho r and greA start/greA end; Supplementary Table S6) and expressed using the pET expression system and purified as described in (6,25).
Velocity of transcription in vivo was measured essentially as
described in (26). Briefly, cells were grown in C + Y medium
to OD600 ∼0.25, transcription of PssbB -luc-gfp was induced
by adding CSP to a final concentration of 100 ng/ml. Samples were withdrawn with 10-s intervals and transcription
was stopped by adding double volume of RNA Protect
reagent (Qiagen). Total RNA was extracted by the hot phenol method and 5 ␮g was used for each dot on northern
dot-blot. Early and late RNA probes were obtained by T7
RNA polymerase on PCR templates made with oligonucleotides luc early start/luc early end t7 (early probe) and
gfp late start/gfp late end t7 (late probe). Dot-blotting was
performed and analyzed as described in (26).
Fluorescence microscopy
Cells were grown at 37◦ C in plastic 5 ml capped tubes,
basically as described previously (27). Microscopy pictures were taken with a Deltavision (Applied Precision)
IX71Microscope (Olympus), using a CoolSNAP HQ2 camera (Princeton Instruments) and a 300 W Xenon light
Nucleic Acids Research, 2014, Vol. 42, No. 17 10989
Figure 1. GreASpn is crucial for normal growth and cell physiology. (A) Sequence alignment of the N-terminal peptides in GreA. The conserved acidic
residues crucial for stimulating RNAP hydrolysis are highlighted in red. Abbreviations of species: Eco, Escherichia coli; Bsub, Bacillus subtilis; Spn, Streptococcus pneumoniae. (B) Growth curves of wild type and greASpn mutant strains grown in C + Y medium. For clarity only every third data point is plotted.
Curves are averages of at least three replicates. Note that the optical density is plotted on a linear scale instead of a log scale to better highlight the differences in cell densities at later time-points when the greASpn mutant lyses. (C) Microscopy analysis of wild type and the greASpn mutant. Yellow arrowheads
highlight anucleate cells.
10990 Nucleic Acids Research, 2014, Vol. 42, No. 17
source through a 100x oil immersion objective (phase contrast). For more details, see the supplementary information.
RNA-Seq
Total RNA was isolated from mid exponentially growing
cells and cDNA sequence libraries were prepared and sequenced as described in detail in the supplemental information. Raw sequence data are deposited and available
on Sysmo-Seek (https://seek.sysmo-db.org Project Noisy
Strep).
Calculating transcription mistakes using RNA-Seq data
Illumina raw sequence error rates are quite high, which
might be caused by natural errors, errors introduced during cDNA library construction and Illumina-specific errors
(28–30). Therefore, raw RNA-Seq data were first filtered to
exclude all reads containing stretches of five or more similar nucleotides in a row (e.g. TTTTT). Furthermore, only
high quality reads were selected by trimming all reads indiscriminately to 95 bp (removing the first and last 20 bases)
and using the fastx toolkit to reject reads on quality (average phred score 25 and no phred scores below 15). Then
the reads for both wild type and greA mutant were aligned
using the Bowtie readmapper (31). The resulting mapped
reads were compared with the reference genome of S. pneumoniae D39 (NCBI annotation ID NC 008533.1), read by
read, position by position using a custom C++ program.
All bases in the reads different from the reference were tallied and an average per read was calculated. These averages
of mismatch rates were a mix between transcription errors
and sequencing errors. It should be valid to compare them
between wild type and mutant because sequencing errors
should on average be the same given the same sequencing
procedure used for both wild type and mutant samples. This
approach was further validated by the fact that error rates
were found to be significantly higher when cDNA libraries
were prepared from the same RNA sample using lower fidelity reverse-transcriptase enzymes.
In vivo fidelity assay
Nonsense suppression was measured by determining ␤galactosidase activity in cultures of pneumococcal strains
carrying a multicopy plasmid containing a lacZ reporter
gene with a premature stop codon early in the coding sequence. Strains were grown at 37◦ C in C + Y medium
supplemented with 0.15 ␮g/ml erythromycin. At appropriate optical density (O.D. 550 = 0.3), cells were harvested,
concentrated 10 times in Z-buffer (60 mM Na2 HPO4 , 40
mM NaH2 PO4 , 10 mM KCL, 1 mM MgSO4 ), flash frozen
in liquid nitrogen and stored at −20◦ C. To determine ␤galactosidase activity, samples were thawed at room temperature and mixed with 30 ug/ml hexadecyl trimethylammonium bromide. After 5 min of incubation at 30◦ C, onitrophenyl-␤-D-galactopyranoside was added to a final
concentration of 364 ␮g/ml. Incubation was continued at
30◦ C and reactions were stopped by addition of 0.23 volumes of 1 M Na2 CO3 . Absorbance was measured at 420
nm. Miller units of ␤-galactosidase activity were calculated
according to the formula: (522 × A420 nm)/(time(min) ×
volume (ml) × O.D. 550). It should be noted that similar
results were obtained by using the protein levels, as determined by a Bradford assay, instead of the O.D. of the samples at time of collection.
Simulations of the stochastic transcription model
The model of Klumpp and Hwa (32) was adjusted in
the following way (also see Supporting text): Elongation
complexes are described as stochastic steppers on a onedimensional lattice. They enter the system at the promoter
with the initiation attempt rate ␣ if the promoter is free,
step forward with the elongation rate ⑀ and leave the system
when reaching the termination site. All steps are rejected if
the target site is occupied by another elongation complex.
At specific (randomly selected) sites, elongation complexes
may undergo transitions to a stalled state with rate f, from
which they are rescued with rate 1/␶ , where ␶ is the duration of the stall event. The effect of GreASpn is described as a
strong reduction of ␶ (or, in alternative scenarios, as a reduction of f or an increase of ⑀). The model was simulated with
the kinetic Monte Carlo approach described in (32), with
a basic time step of 0.01 s. Elongation measurements were
simulated by starting with an empty lattice and averaged
over 1000 simulation runs. To study the dependence on expression level (modulated by varying ␣) and the gene length,
the simulations were allowed to reach the steady state and
transcription rates were obtained as time averages over 4.5
× 106 Monte Carlo steps.
RESULTS
GreASpn is crucial for cellular growth and cell morphology
To characterize the function of GreASpn in vivo we replaced
greASpn with a chloramphenicol resistance cassette resulting in strain PGs6 (see supplementary methods). Besides
reduced growth within agar plates (not shown), greASpn
cells showed a significant increased doubling time in liquid
C + Y media (43 ± 3 min for greASpn versus 28 ± 1 min
for wild type, ± indicates standard deviation). Furthermore,
cultures did not reach the same cell density as wild-type
cells, and OD600 started to drop after prolonged incubation
(Figure 1B). Microscopy analyses (not shown) revealed that
the drop in OD600 was caused by cell lysis rather than by
cell clumping. Note that these growth curves were started by
diluting exponentially growing cells, and not directly from
frozen stocks and thus do not reflect a decreased ability to
survive freezing.
To exclude the possibility that the observed growth defects were due to a polar effect of the chloramphenicol resistance cassette, we introduced a copy of greASpn
at the ectopic, nonessential, bgaA locus, under the control of a Zn2+ inducible promoter (PZn ), resulting in
strain PGs48 (greASpn , bgaA::PZn -greASpn ). In the presence of 75 ␮M of Zn2+ , normal cell growth was restored (Figure 1B). Wild-type cells grew identically in the
absence or presence of added Zn2+ (data not shown).
To test whether the catalytic activity of GreASpn is required for normal growth, we cloned the catalytically
Nucleic Acids Research, 2014, Vol. 42, No. 17 10991
inactive greASpn-D43A/E46A mutant allele under the control of PZn and integrated this construct at the bgaA locus in an otherwise greASpn background (strain PGs67:
greASpn , bgaA::PZn -greASpn-D43A/E46A ). Strikingly, induction of GreASpn-D43A/E46A with 75 ␮M of Zn2+ resulted
in even stronger growth defects than without zinc (Figure 1B), suggesting that the presence of inactive GreA at
RNAPs catalytic site perturbs RNA polymerase functions.
This observation is consistent with our earlier results, which
showed that catalytically deficient GreA further ‘switches
off’ intrinsic hydrolytic activity of RNAP by sequestering
the Trigger Loop of the active center (3). However, although
we used the minimal concentration of Zn2+ sufficient for full
complementation in PGs48, we cannot formally exclude the
possibility that zinc-induced GreASpn-D43A/E46A is present at
higher than wild-type GreA levels. Note that for reasons
currently unknown, cell lysis after prolonged incubation
(>600 min) was less pronounced in strain PGs67 (greASpn ,
bgaA::PZn -greASpn-D43A/E46A ) compared to the greA mutant
(PGs6) (Figure 1B).
To examine the effects of greASpn deletion on cell morphology, strain PGs6 (greASpn ) and the wild type parental
strain (D39) were grown in liquid C + Y medium at
37◦ C and cells were harvested for microscopy at midexponential growth. DNA was stained with 4’,6-diamidino2-phenylindole (DAPI) and membranes were stained with
the lipophilic Nile red dye. As shown in Figure 1C, greASpn
cells exhibited a pleiotropic array of cell morphologies including chains of cells, small cells and large cells. In line
with a defect in transcription, DAPI staining showed the
occasional presence of anucleate cells in the greASpn mutant (∼2.8%, >1000 cells counted, (33)), whereas this was
never the case for the wild type (Figure 1C). Complementation of greASpn with 75 ␮M of Zn2+ in the PGs48 strain
resulted in normal cell morphology (Supplementary Figure
S1). Together, these data demonstrate that the activity of
GreASpn is crucial for normal growth and cell physiology in
S. pneumoniae.
Absence of GreASpn slightly increases in vivo error rate in
gene decoding
In vitro data suggest that Gre-factors play an important role
in transcription fidelity (2,3). The above-mentioned results
show that cells lacking greASpn are significantly perturbed
in their physiology. This may be caused by an increased rate
of transcriptional errors. To directly examine if transcription fidelity is affected by greASpn deletion in vivo, we constructed a reporter cassette that contains a constitutively expressed lacZ gene containing a stop codon mutation (P32 lacZG15stop ). Functional LacZ will thus only be produced
if errors in transcription or translation are regularly made.
The lacZ-fidelity reporter was introduced on a replicative
multicopy vector, resulting in plasmid pPGs6. The bgaA locus, encoding the only endogenous galactosidase of S. pneumoniae, was deleted in both wild type and greASpn to reduce background activity in LacZ assays. As shown in Figure 2A, a small, though significant, difference in production
of functional LacZ was observed in the absence of GreASpn
(<145% of wild type; P < 0.05, t-test, 8 replicates) and fi-
Figure 2. Absence of GreASpn slightly increases in vivo error rate in gene
decoding. (A) Normalized ␤-galactosidase activity levels of a wild type
and a greA strain carrying a multicopy plasmid with a constitutively expressed lacZ reporter gene with a premature stop codon early in the coding
sequence. Values shown are averages of three independent replicates (error
bars: standard deviation). (B) RNA-Seq data were filtered to only include
high quality reads. Transcription errors were tallied using a custom Python
script by comparing the sequence of the mapped reads to the corresponding positions in the reference genome of S. pneumoniae D39 and the percentage of high quality reads that misaligned are shown (see Materials and
Methods).
delity could be restored by GreASpn complementation (Supplementary Figure S2).
To further investigate the effects of GreASpn deletion on
fidelity of transcription, we performed transcriptome sequencing (RNA-Seq). Total RNA was isolated from midexponentially growing cultures of wild type (D39) and
greASpn (PGs6) in C + Y medium at 37◦ C and sequencing libraries were prepared (see Materials and Methods).
To assess whether this methodology would allow us to
detect transcription mistakes, we reverse transcribed total
RNA with Moloney murine leukemia virus (M-MLV) reverse transcriptase (RT) and with a variant of the same
enzyme called AccuScript RT which has approximately 3to 6-fold increased fidelity according to the supplier (Agilent). The cDNA libraries were subsequently sequenced using Illumina sequencing (see Materials and Methods for
details). After rigorous filtering of the reads (see Materials and Methods), we found that the cDNA libraries prepared with AccuScript RT had an overall lower mismatch
rate when aligned to the reference genome sequence (GenBank Acc. NC 008533.1) compared to the cDNA libraries
prepared with M-MLV (Figure 2B) thus validating this approach. If the greASpn mutant generates more mistakes
during transcription than the wild type, this should be reflected in an increase of the overall mismatch rate of the
RNA-Seq reads. Indeed, we found an overall mismatch rate
of 0.24 and 0.55% in the greASpn mutant of cDNA libraries prepared with either AccuScript or M-MLV compared to 0.23 and 0.50% of errors in the wild type demonstrating slightly more transcription errors in the absence of
10992 Nucleic Acids Research, 2014, Vol. 42, No. 17
GreASpn (Figure 2B). The observed mismatch rate is much
higher than previously determined transcription error rates
(∼10−5 per nucleotide; (34)) which are generally based on
assays similar as the aforementioned lacZ assay and are
thus difficult to extrapolate over the entire transcriptome.
However, the error rate found here is exaggerated by mistakes introduced during reverse transcription, PCR and the
process of Illumina sequencing itself (also see Supplementary Material). Because of the small differences observed
by the RNA-seq analysis we cannot make any quantitative
conclusions regarding the extent of reduced fidelity of the
greA mutant. However, Stevens et al. showed that a 2-fold
increase in decoding errors (which is much larger than observed here) causes only mild negative effects on pneumococcal cell growth (35). Taken together, we can infer that
reduced transcription fidelity cannot be the major source
of the observed pleiotropic effects on cell physiology of
greASpn cells. Note, however, that misincorporation events
lead to paused complexes formation, which may have more
detrimental effects than the RNA sequence alterations (see
below).
Highly expressed genes are more sensitive to lack of GreASpn
Analysis of the RNA-Seq data revealed that more than
25% of the genome was more than 2-fold differentially expressed in the greASpn mutant (Supplementary Table S1).
Roughly half of these genes were more than 2-fold upregulated and about half were more than 2-fold downregulated (Supplementary Table S1). Real-time quantitative
PCR on RNA isolated from wild type and greASpn cells
using primers for a selected set of genes verified the RNASeq results (Supplementary Table S2). Among the upregulated genes were the genes belonging to the CiaR/H regulon (e.g. htrA, SPD 0775 and SPD 0913), which is activated upon envelope stress (36,37); the heat shock induced
HrcA regulon (e.g. clpL and dnaK) and genes involved in
DNA-repair (e.g. dprA, ssb and SPD 0715) indicating a potential conflict between replication and transcription in the
absence of GreASpn (Supplementary Table S3). Also, the
ccpA gene, encoding for the global catabolite repressor protein was downregulated (Supplementary Table S2). Likely
because of ccpA downregulation, over one third (38 genes)
of its core regulon (38) were subsequently more than 2fold up- or downregulated (Supplementary Table S3). Perturbed expression of this global regulator alone could already account for more than 7% of all the differentially regulated genes in the greASpn mutant (Supplementary Table
S3). Other noteworthy downregulated genes are involved in
DNA-replication (e.g. purC, ogt and dnaX), cell wall synthesis (e.g. glmM, pbp1A and pbp1B) and protein synthesis (e.g.
rpsT, efTU and prfC). Potential differences in mRNA stability in the greASpn mutant are unlikely to affect the expression patterns, as it was shown that, at any growth phase, the
impact of synthesis greatly outweighs the impact of degradation on the level of all mRNAs studied (39).
Interestingly, when we plotted the fold change difference
between greASpn and wild type as a function of gene expression strength (similar to a MA-plot (40)), a clear trend
is visible in that highly expressed genes were affected more
by the absence of GreASpn than lowly expressed genes (Fig-
Figure 3. Highly expressed genes are more sensitive to lack of GreASpn . (A)
RNA-Seq data plotted as an MA-plot. Each dot represents the expression
value and fold change of a single Open Reading Frame (ORF). Note that
some outliers fall outside the plotted area (<1% of all data points). A linear
regression line (R = 0.36) is shown in red. (B) Effects of greASpn on the
expression of Green Fluorescent Protein (GFP) driven by synthetic constitutive promoters. Expression of GFP is disproportionally reduced when
the promoter is stronger in the greASpn mutant and the differences are
smaller when the promoter is less strong. (C) Differential expression in the
greASpn mutant is not correlated with gene length.
ure 3A, R = 0.36). Note however that this trend did not
hold for the strongly expressed ribosomal RNAs (rRNAs)
(Supplementary Table S4; see Discussion). To validate this
Nucleic Acids Research, 2014, Vol. 42, No. 17 10993
observation, we constructed a set of strains carrying synthetic constitutive promoters of different strengths driving
Green Fluorescent Protein (GFP) in both the wild type
and greASpn genetic backgrounds and measured total fluorescence as a proxy for transcription rate. The effect of
GreASpn deletion on these promoters reaffirmed the observations at the genomic level: strongly expressed genes were
more strongly affected (Figure 3B). Interestingly, we observed no correlation between gene length and GreASpn dependency (Figure 3C, R = 0.03; see below). Together, these
results suggest that, in general, gene expression is reduced
in the absence of GreASpn , resulting in knock-on effects that
may lead to gene upregulation, such as in the case of the
CcpA regulon. These global transcriptional changes may
ultimately be responsible for the pleiotropic phenotypes displayed by greASpn mutant cells (Figure 1).
Transcription initiation and promoter escape are not influenced by GreASpn
We next analyzed the steps of the transcription cycle (initiation, promoter escape or elongation) affected by the absence
of GreASpn . First, we tested if the absence of GreASpn influences open promoter complex formation in vitro using purified GreASpn and holo RNAPSpn . We used a PCR fragment
carrying the promoter of the purC gene, which, according
to the RNA-seq and qRT-PCR data, was strongly affected
by GreASpn deletion (Supplementary Tables S2 and S3).
Open complexes were probed with KMnO4 , which modifies thymine bases only in single stranded regions of DNA.
As seen from Figure 4A, the presence of GreASpn did not
influence open complex formation in vitro.
Next, we analyzed abortive initiation with or without
GreASpn or mutant GreASpn-D43A/E46A on several templates
(ccpA, purC and SPD 0267) whose gene expression was decreased in the greASpn mutant (Supplementary Tables S2–
S3). To do so, we monitored extension of a dinucleotide
primer with a radiolabeled nucleoside monophosphate. The
experiment demonstrated that GreASpn does not affect this
stage of transcription (Figure 4B). GreA from E. coli was
proposed to increase the efficiency of promoter escape (13).
Therefore, we tested in vitro transcription on the same
templates. As shown in Figure 4C the pattern of abortive
transcripts formed during promoter escape or the ratio of
abortive transcripts to the run-off RNA (product of transcription till the end of the linear template) were the same in
the presence or absence of GreASpn . As expected, a number
of cleavage products appeared when GreASpn was present
in the reaction. We therefore conclude that GreASpn has no,
or minor effects on transcription initiation and promoter
escape.
GreASpn stimulates production of full-length transcripts
through suppression of transcription pauses
Curiously, the amount of 100 nt run-off RNAs on the templates used in the above experiment was not increased by
the presence of GreASpn (e.g. compare lanes 1 and 2 in Figure 4C). This result apparently contradicts the RNA-seq
data that show increased transcription of these genes in the
wild-type strain (in the presence of GreASpn ). We hypothesized that GreASpn action becomes apparent only during
transcription of the full-length transcripts, i.e. transcription
further downstream of the +100 register. We therefore analyzed transcription in vitro on templates carrying full-length
ccpA, purC and SPD 0267 DNA sequences. In agreement
with our hypothesis, in the presence of GreASpn , the runoff transcripts on these templates accumulated much more
readily than in its absence (Figure 5A). Earlier, we observed
that RNAPSpn has lower processivity on some sequences
than bacterial RNAPs from E. coli or Thermus aquaticus
(41). Indeed, specific transcriptional pauses or arrests could
be seen in the in vitro transcription assays in the absence of
GreASpn (Figure 5A, red dots).
The in vivo analysis suggested that GreASpn does not
strongly contribute to the sequence correctness of produced
RNA, which apparently is achieved by the accuracy of
RNAPSpn active center (42). However, misincorporation
events, if not immediately resolved, lead to strong pausing of transcription (2,3,5). We tested if GreASpn can suppress misincorporation pausing by stimulating RNAPSpn to
cleave the erroneous RNA. To do so, we used artificial elongation complexes that were assembled from synthetic template and nontemplate DNA oligonucleotides (fully complementary to each other) and 5 end radiolabeled RNA
oligonucleotides (see scheme in Figure 5B). Such complexes were shown to be indistinguishable from the elongation complexes obtained by transcription from a promoter
(4,5,43). Assembled RNAPSpn elongation complexes were
forced to misincorporate ATP at the dCMP base in the
DNA template and then (without washing the complexes)
allowed to elongate in the presence of all NTPs in either
the presence or absence of GreASpn . Elongation in the absence of GreASpn leads to a strong pause in further extension of erroneous transcript (Figure 5B). This pause was absent in the presence of GreASpn (Figure 5B) indicating that
GreASpn can also contribute to processivity of elongation by
suppressing the pauses caused by misincorporation as was
also shown for E. coli GreA and T. aquaticus GreA (2,5).
GreASpn does not influence the rate of elongation
So far, our results indicate that GreASpn facilitates processivity of transcription elongation by suppressing pausing
by RNAPSpn . However, it is unclear whether this suppression leads to an increased rate of transcription (if GreASpn
suppresses short-living pauses) or increases the chance of
RNAP finishing transcription of a gene; two kinetically
distinct scenarios. To distinguish between these scenarios,
we examined the influence of GreASpn on the velocity of
RNAPSpn elongation in vivo. To do so, we compared kinetics of synthesis of 5 - versus 3 -proximal part of an inducible genomic ∼2700 bp long luc-gfp reporter transcript
in wild type and greASpn strains. The method involves isolation of total RNA at various time intervals after induction
of the CSP-inducible PssbB promoter and northern blotting
with probes complementary to the 5 and 3 ends of the lucgfp transcript. The time elapsed between the appearances
of the two signals after addition of inducer is used to estimate transcription elongation velocity (26,44). As shown
in Figure 5C, the elongation kinetics were very similar for
both wild type and greASpn strains (62 ± 5 versus 58 ± 5
nt/s, respectively), suggesting that GreASpn has little effect
10994 Nucleic Acids Research, 2014, Vol. 42, No. 17
Figure 4. Transcription initiation and promoter escape are not influenced by GreASpn. (A) Open complexes of the purC promoter formed by RNAPSpn
in the presence or absence of GreASpn were probed with KMnO4 . A + G reaction was used as a marker. (B)In vitro abortive initiation on ccpA, purC and
SPD 0267 with or without GreASpn or mutant GreASpn-D43A/E46A . (C). Products of in vitro transcription on short (resulting in ∼100 nt-long run off) ccpA,
purC and SPD 0267 with or without GreASpn or mutant GreASpn-D43A/E46A were separated on 15 and 33% denaturing gels to visualize run off and abortive
products, respectively. Short cleavage products at the bottom of the gel in the presence of GreASpn originate from cleavage in elongation complexes, as no
cleavage is seen in the abortive initiation assay (panel B). Additional low mobility bands in the presence of GreASpn are thought to be cleavage products of
the longer transcripts, in particular the full length ones that are known to be retained in the elongation complex at the ends of templates. Note that pauses
in the presence or absence of GreASpn-D43A/E46A are similar and the apparent differences are attributed to the contrast of the image.
on the speed of transcribing RNAPSpn . However, in contrast
to the wild type, the slope of the emerging 3 probe signal
is less steep than that of the signal emerging for the 5 end
probe. This cannot be explained by altered Rho-dependent
polarity, since S. pneumoniae does not have a Rho factor
(20,21). Therefore, this result indicates that not all of the
RNAPs that started transcription from the promoter were
able to reach the terminator in the greASpn strain within
the time of the experiment. Thus, we conclude that GreASpn
suppresses the long-living or dead-end pauses, which otherwise preclude RNAPSpn from finishing transcription.
Development and characterization of a stochastic model of
GreASpn -dependent transcription
The above results suggest that GreASpn augments transcription by restarting stalled elongation complexes. However,
this does not intuitively explain why highly expressed genes
are particularly sensitive to the lack of GreASpn while longer
genes are not (Figure 3). To strengthen this conclusion and
to gain more insights in the molecular mechanism involved,
we developed a stochastic model of transcription in the
presence and absence of GreASpn . We built a model upon
a framework previously established to model the effects of
pausing, termination and antitermination on rRNA transcription in E. coli (32). The model takes into account that
GreASpn does not affect initiation and promoter escape. We
used the model to test three scenarios, where stimulation of
transcript elongation by GreASpn is due to either an increase
of the stepping rate (the elongation rate without pauses), a
reduction of the duration of the pauses or of the frequency
of pauses (Figure 6, Supplementary Figure S3). Simulations
of all three scenarios were consistent with the experimental
observations that: highly expressed genes are more sensitive
to the lack of GreASpn (Figure 6A, Supplementary Figure
S3A and D); and that absence of GreASpn does not significantly impact the expression of longer genes (Figure 6B.
Supplementary Figure S3B and E). However, only the sim-
Nucleic Acids Research, 2014, Vol. 42, No. 17 10995
Figure 5. GreASpn stimulates elongation. (A) Run off in vitro transcription with or without GreASpn on DNA-templates carrying full-length ccpA, purC
and SPD 0267 (compare to Figure 4C). Red dots highlight transcription pauses released in the presence of GreASpn . (B) Elongation complex was assembled
from complementary template and nontemplate DNA oligonucleotides and 5 end labeled RNA. These complexes were forced to misincorporate ATP for
30 s. After that (without washing) NTPs were added in either the presence or absence of GreASpn . Note that the fainter mEC14 band at ‘0’ time point
is caused by loading. The top bands are a few nucleotides shorter than the expected run off product, likely due to immobilization of the complexes on
streptavidin beads through biotin at the 5 . (C) To analyze the rate of transcription elongation in vivo, total RNA was isolated at various time intervals after
induction of the CSP-inducible PssbB promoter and northern blotting with probes complementary to the 5 and 3 ends of the luc-gfp transcript (scheme at
the top). Representative dot blots of the early and late probes are shown above the plots. The rate of transcription is calculated as the distance between the
probes divided by the time between the emergences of signals of early and late probes. Later emergence of the probes signals in the mutant strain (closed
symbols) compared to the wild type (open symbols) could be due to altered timing of induction of the PssbB promoter, which involves several steps, which
may in their turn be affected by deletion of GreASpn . This however does not affect the rate of elongation.
10996 Nucleic Acids Research, 2014, Vol. 42, No. 17
Figure 6. Stochastic transcription model predicts the presence of transcription traffic jams in the absence of GreASpn . Data from simulations with and
without GreA (mimicked by short- and long-stalling events) for different expression levels (in the simulations, we use the transcription rate as a measure
of gene expression. This is proportional to the mRNA concentration measured in the experiments of Figure 3) (A) and different gene lengths (B). (C)
Simulated elongation experiments: Amount of RNA synthesized as a function of time. The time that elapses between synthesis of an early probe (solid
lines) and a late probe (dashed lines) reflects the elongation speed. Lack of GreASpn results in a decrease of the number of elongation complexes that
reach the late probe (compare to Figure 5C). (D) Graphical representation of simulation time courses (kymographs): Each green dot indicates an active
elongation complex, red dots indicate stalled complexes. Traffic jams of active elongation complexes form transiently behind stalled complexes.
ulations where GreASpn was taken to affect pausing could
recover the observed pattern of the elongation experiment
and show that less RNAPs reach the end of the gene in the
absence of GreASpn as indicated by the lower slope in the
accumulation of the 3 probe’ (Figure 6C, Supplementary
Figure S3C and F). The simulations cannot definitely distinguish whether GreASpn reduced the duration or the frequency of pauses, although the agreement with the elongation experiments is slightly better for the pause-duration
scenario. Based on the known mechanism of Gre-factors
in E. coli (2,5,10), we consider a reduction of the pause
duration as more likely. Moreover, simulations of the scenario of a reduced pause frequency requires very rare, but
very long pauses in the wild type, for which we have no
evidence. Thus, the simulations provide additional support
for our interpretation of the data in Figures 4 and 5. Importantly, simulations explain the reduced transcription in
the absence of GreASpn by the formation of transcription
‘traffic jams.’ These traffic jams are formed by RNAPs that
queue behind the paused elongation complex (Figure 6D).
The model also predicts that averaged velocity of transcription elongation (i.e. including pauses and traffic jams) is the
main rate-limiting factor for highly expressed genes (Supplementary Figure S4).
DISCUSSION
Bacterial Gre-factors have been mostly studied in E. coli
and it has become clear that, at least in vitro, Gre-factors
stimulate RNAPs intrinsic proofreading activity and in that
way speed up transcription and seem to be involved in
nearly all steps of transcription: initiation, elongation and
fidelity. The consequences of the lack of Gre-factor for the
cell’s physiology in vivo, however, have remained largely elusive. This can be partly explained because of the genetic redundancy present: E. coli contains two Gre-factors, GreA
and GreB and several additional regulators, DksA, Rnk
and TraR, which can also bind to the secondary channel
of RNAP (15–17). Thus, interpretation of the physiological
function of Gre has turned out to be difficult (18). Here, we
studied the in vivo role of the Gre-factor of S. pneumoniae,
an organism that only contains one Gre-factor, GreASpn
and no other homologs. Furthermore, the S. pneumoniae
genome does not code for any other secondary channel
binding homologs of Gre. The importance of a functional
Gre-factor became immediately clear by analyzing cells of
the greASpn knockout mutant, which are severely perturbed
in their growth (Figure 1). Importantly, we were not able to
pick up fast growing suppressors by plate or liquid growth
assays (data not shown), indicating that single mutations
in RNAP or elsewhere in the genome cannot compensate
Nucleic Acids Research, 2014, Vol. 42, No. 17 10997
Figure 7. Model for the in vivo function of GreASpn . RNAP stalls frequently, for instance due to misincorporation events. In the absence of
GreA (red ‘do not enter’ symbol), queues of RNAP arise leading to altered
gene expression and reduced growth. These stalled elongation complexes
might also cause DNA-damage by collisions with the replication machinery (48,49).
for the loss of function of GreASpn . Together with RNASeq and biochemical assays we obtained a clear picture of
the in vitro and in vivo characteristics of GreASpn and the
ramifications for cells to live without GreASpn (Figures 2–
5). These data allowed us to formulate a stochastic mathematical model of transcription, which predicts that in the
absence of GreASpn , RNAP queues rapidly arise and that
these traffic jams compromise gene expression (Figure 6).
Interestingly, the model could also reproduce the observation that highly expressed genes were downregulated relatively more than lowly expressed genes (Figure 3). A likely
explanation for this phenomenon is that highly expressed
genes initiate transcription very frequently and thus the total transcription rate is rather limited by the elongation rate
(Supplementary Figure S4), while lowly expressed genes fire
transcription infrequently and their total transcription rates
are thus limited by transcription initiation; which we show
is not affected in the absence of GreASpn (Figure 4).
RNAPs and ribosomes were proposed to cooperate to
rescue backtracked RNAPs by ‘pushing’ them forward
(26,45). This model however would predict that in the presence of excessive backtracking the highly expressed genes
must be affected less by the absence of GreA than low-
expressed genes, because the trailing RNAPs and ribosomes
would ‘push’ paused RNAP from backtracking. Our data
show the contrary pattern of transcription, suggesting that
the cooperation of RNAPs and ribosomes to rescue backtracked complexes may not be efficient enough to suppress deletion of GreASpn . Instead our results suggest that
the paused RNAP rather causes queuing of the trailing
RNAPs. The situation might be different for rRNAs that
are not translated and are not reduced in the greASpn mutant, whereas genes encoding ribosomal proteins are downregulated (Supplementary Table S4). The rate of transcription elongation on rRNAs is roughly 2-fold higher than that
of protein coding genes (46,47). Fast transcription suggests
smaller amount of pausing sites thus decreasing the chances
of backtracking. In addition, extensive secondary structures
forming in the nascent rRNAs would physically block extensive backtracking. These properties may alleviate the necessity of GreASpn . Whether this hypothesis is correct requires further investigation.
Backtracked transcription elongation complexes, which
can be resolved by Gre-factors, were proposed to cause
DNA-damage by collisions with the DNA-replication machinery (48,49). The collisions may also be suppressed by
point mutations in RNAP that were proposed to destabilize elongation complexes and/or reduce backtracking
(48). The absence of slow-growth suppressors in the greASpn
knockout mutant however, suggests that S. pneumoniae
RNAP cannot easily overcome backtracking possibly because it is poorly processive (e.g. compared to E. coli
RNAP; (41)). Our data also suggest that queues of RNAPs,
rather than single backtracked RNAP, may be a major
problem for replication fork progression.
Transcription initiation is generally assumed to be rate
limiting and the most regulated step of gene expression.
Here, we present evidence that elongation of transcription
can also determine the rate of gene expression and, thus,
represent a major point for gene regulation in S. pneumoniae
and possibly in other bacteria. Indeed, many transcription
elongation factors such as ␭N and ␭Q (of bacteriophage ␭),
NusA and NusG (of E. coli) that affect transcription elongation have been shown to affect gene expression (50). Interestingly, it was recently shown that the transcription rate of
eukaryotic RNAPII (Pol II) varies on genes demonstrating
that elongation is also a regulated rate-limiting step during
transcription in higher organisms (51).
Together, we now propose the following model for the
major role of bacterial Gre-cleavage factors in vivo (Figure 7). After initiation of transcription and promoter escape, RNAP pauses on intrinsic signals (52) or as a result of
misincorporation. These stalls, if not resolved, might lead to
backtracking and cause queues of RNAP and, as a result,
transcription ‘traffic jams’ arise (Figure 7). Importantly,
these queues or traffic jams of RNAPs dramatically hamper gene expression and are detrimental for the cell. Future
experiments, for instance chromatin immunoprecipitation
assays using RNAPSpn antibodies and nascent transcript sequencing could be performed to test our model. Since our
model predicts stochastic backtracking by RNAPs, single
molecule and single-cell experiments might be more informative than bulk assays, and this is something we are currently pursuing. Indeed, nascent RNA-sequencing experi-
10998 Nucleic Acids Research, 2014, Vol. 42, No. 17
ments showed no differences in RNAPEco pausing in the absence of Gre indicating that backtracked pauses may occur
randomly (52).
While S. pneumoniae provides a useful (minimal) model
organism to study the bacterial transcription cycle in vivo,
S. pneumoniae is also a serious human pathogen annually
killing nearly 1 million children (53). Furthermore, over the
last decades, S. pneumoniae resistance to existing antibiotics
has spread and is now a serious problem (54). Our results
reveal Gre-factor as a possible target for innovative drug
design. Furthermore, our unique methodology, combining
experimental methods on both the molecular (biochemical)
level and the systems level and mathematical modeling, may
serve as an example for studies on unrelated systems.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
We thank Harma Karsens for the construction of the P32 gfp constructs and Jean-Pierre Claverys for the luc gene. We
thank Anne de Jong and Fritz Thuemmler for help with
RNA-Seq data analysis.
FUNDING
Sysmo2 Grant (Noisy Strep) [to J.W.V., N.Z.]; European
Research Council [337399-PneumoCell to J.W.V., ERC2007-StG 202994-MTP to N.Z.]; Netherlands Organisation for Scientific Research (NWO-ALW VIDI) [864.12.001
to J.W.V.]; UK Biotechnology and Biological Sciences Research Council [to N.Z]. Funding for open access charge:
NWO.
Conflict of interest statement. None declared.
REFERENCES
1. Komissarova,N. and Kashlev,M. (1997) Transcriptional arrest:
Escherichia coli RNA polymerase translocates backward, leaving the
3’ end of the RNA intact and extruded. Proc. Natl. Acad. Sci. U.S.A.,
94, 1755–1760.
2. Erie,D.A., Hajiseyedjavadi,O., Young,M.C. and von Hippel,P.H.
(1993) Multiple RNA polymerase conformations and GreA: control
of the fidelity of transcription. Science, 262, 867–873.
3. Roghanian,M., Yuzenkova,Y. and Zenkin,N. (2011) Controlled
interplay between trigger loop and Gre factor in the RNA polymerase
active centre. Nucleic Acids Res., 39, 4352–4359.
4. Yuzenkova,Y. and Zenkin,N. (2010) Central role of the RNA
polymerase trigger loop in intrinsic RNA hydrolysis. Proc. Natl.
Acad. Sci. U.S.A, 107, 10878–10883.
5. Zenkin,N., Yuzenkova,Y. and Severinov,K. (2006) Transcript-assisted
transcriptional proofreading. Science, 313, 518–520.
6. Laptenko,O., Lee,J., Lomakin,I. and Borukhov,S. (2003) Transcript
cleavage factors GreA and GreB act as transient catalytic
components of RNA polymerase. EMBO J., 22, 6322–6334.
7. Opalka,N., Chlenov,M., Chacon,P., Rice,W.J., Wriggers,W. and
Darst,S.A. (2003) Structure and function of the transcription
elongation factor GreB bound to bacterial RNA polymerase. Cell,
114, 335–345.
8. Sosunova,E., Sosunov,V., Kozlov,M., Nikiforov,V., Goldfarb,A. and
Mustaev,A. (2003) Donation of catalytic residues to RNA
polymerase active center by transcription factor Gre. Proc. Natl.
Acad. Sci. U.S.A., 100, 15469–15474.
9. Tagami,S., Sekine,S.-I., Kumarevel,T., Hino,N., Murayama,Y.,
Kamegamori,S., Yamamoto,M., Sakamoto,K. and Yokoyama,S.
(2010) Crystal structure of bacterial RNA polymerase bound with a
transcription inhibitor protein. Nature, 468, 978–982.
10. Borukhov,S., Sagitov,V. and Goldfarb,A. (1993) Transcript cleavage
factors from E. coli. Cell, 72, 459–466.
11. Toulmé,F., Mosrin-Huaman,C., Sparkowski,J., Das,A., Leng,M. and
Rahmouni,A.R. (2000) GreA and GreB proteins revive backtracked
RNA polymerase in vivo by promoting transcript trimming. EMBO
J., 19, 6853–6859.
12. Gordon,A.J., Halliday,J.A., Blankschien,M.D., Burns,P.A.,
Yatagai,F. and Herman,C. (2009) Transcriptional infidelity promotes
heritable phenotypic change in a bistable gene network. PLoS.Biol.,
7, e44.
13. Stepanova,E., Lee,J., Ozerova,M., Semenova,E., Datsenko,K.,
Wanner,B.L., Severinov,K. and Borukhov,S. (2007) Analysis of
promoter targets for Escherichia coli transcription elongation factor
GreA in vivo and in vitro. J. Bacteriol., 189, 8772–8785.
14. Goldman,S.R., Ebright,R.H. and Nickels,B.E. (2009) Direct
detection of abortive RNA transcripts in vivo. Science, 324, 927–928.
15. Blankschien,M.D., Potrykus,K., Grace,E., Choudhary,A.,
Vinella,D., Cashel,M. and Herman,C. (2009) TraR, a homolog of a
RNAP secondary channel interactor, modulates transcription. PLoS
Genet., 5, e1000345.
16. Lamour,V., Rutherford,S.T., Kuznedelov,K., Ramagopal,U.A.,
Gourse,R.L., Severinov,K. and Darst,S.A. (2008) Crystal structure of
Escherichia coli Rnk, a new RNA polymerase-interacting protein. J.
Mol. Biol., 383, 367–379.
17. Rutherford,S.T., Lemke,J.J., Vrentas,C.E., Gaal,T., Ross,W. and
Gourse,R.L. (2007) Effects of DksA, GreA, and GreB on
transcription initiation: insights into the mechanisms of factors that
bind in the secondary channel of RNA polymerase. J. Mol. Biol., 366,
1243–1257.
18. Vinella,D., Potrykus,K., Murphy,H. and Cashel,M. (2012) Effects on
growth by changes of the balance between GreA, GreB, and DksA
suggest mutual competition and functional redundancy in
Escherichia coli. J. Bacteriol., 194, 261–273.
19. Kusuya,Y., Kurokawa,K., Ishikawa,S., Ogasawara,N. and Oshima,T.
(2011) Transcription factor GreA contributes to resolving
promoter-proximal pausing of RNA polymerase in Bacillus subtilis
cells. J. Bacteriol., 193, 3090–3099.
20. Hoskins,J., Alborn,W.E., Arnold,J., Blaszczak,L.C., Burgett,S.,
DeHoff,B.S., Estrem,S.T., Fritz,L., Fu,D.J., Fuller,W. et al. (2001)
Genome of the bacterium Streptococcus pneumoniae strain R6. J.
Bacteriol., 183, 5709–5717.
21. Lanie,J.A., Ng,W.L., Kazmierczak,K.M., Andrzejewski,T.M.,
Davidsen,T.M., Wayne,K.J., Tettelin,H., Glass,J.I. and Winkler,M.E.
(2007) Genome sequence of Avery’s virulent serotype 2 strain D39 of
Streptococcus pneumoniae and comparison with that of
unencapsulated laboratory strain R6. J. Bacteriol., 189, 38 –51.
22. Claverys,J.P., Martin,B. and Polard,P. (2009) The genetic
transformation machinery: composition, localization, and
mechanism. FEMS Microbiol. Rev., 33, 643–656.
23. Martin,B., Garcia,P., Castanie,M.P. and Claverys,J.P. (1995) The recA
gene of Streptococcus pneumoniae is part of a competence-induced
operon and controls lysogenic induction. Mol. Microbiol., 15,
367–379.
24. Kashlev,M., Nudler,E., Severinov,K., Borukhov,S., Komissarova,N.
and Goldfarb,A. (1996) Histidine-tagged RNA polymerase of
Escherichia coli and transcription in solid phase. Meth. Enzymol.,
274, 326–334.
25. Zenkin,N., Kulbachinskiy,A., Yuzenkova,Y., Mustaev,A., Bass,I.,
Severinov,K. and Brodolin,K. (2007) Region 1.2 of the RNA
polymerase sigma subunit controls recognition of the -10 promoter
element. EMBO J., 26, 955–964.
26. Proshkin,S., Rahmouni,A.R., Mironov,A. and Nudler,E. (2010)
Cooperation between translating ribosomes and RNA polymerase in
transcription elongation. Science, 328, 504–508.
27. de Jong,I., Beilharz,K., Kuipers,O.P. and Veening,J.W. (2011) Live
Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using
Automated Time-lapse Microscopy. J. Vis. Exp., 3145,
doi:10.3791/3145.
Nucleic Acids Research, 2014, Vol. 42, No. 17 10999
28. Meacham,F., Boffelli,D., Dhahbi,J., Martin,D.I., Singer,M. and
Pachter,L. (2011) Identification and correction of systematic error in
high-throughput sequence data. BMC Bioinformatics, 12, 1–11.
29. Minoche,A.E., Dohm,J.C. and Himmelbauer,H. (2011) Evaluation of
genomic high-throughput sequencing data generated on Illumina
HiSeq and Genome Analyzer systems. Genome Biol., 12, R112.
30. Nakamura,K., Oshima,T., Morimoto,T., Ikeda,S., Yoshikawa,H.,
Shiwa,Y., Ishikawa,S., Linak,M.C., Hirai,A., Takahashi,H. et al.
(2011) Sequence-specific error profile of Illumina sequencers. Nucleic
Acids Res., 30, e90.
31. Langmead,B., Trapnell,C., Pop,M. and Salzberg,S.L. (2009) Ultrafast
and memory-efficient alignment of short DNA sequences to the
human genome. Genome Biol., 10, R25.
32. Klumpp,S. and Hwa,T. (2008) Stochasticity and traffic jams in the
transcription of ribosomal RNA: intriguing role of termination and
antitermination. Proc. Natl. Acad. Sci. U.S.A., 105, 18159–18164.
33. Kjos,M. and Veening,J.-W. (2014) Tracking of chromosome dynamics
in live Streptococcus pneumoniae reveals that transcription promotes
chromosome segregation. Mol. Microbiol., 91, 1088–1105.
34. Ninio,J. (1991) Connections between translation, transcription and
replication error-rates. Biochimie, 73, 1517–1523.
35. Stevens,K.E., Chang,D., Zwack,E.E. and Sebert,M.E. (2011)
Competence in Streptococcus pneumoniae is regulated by the rate of
ribosomal decoding errors. MBio, 2, e00071-11.
36. Halfmann,A., Kovacs,M., Hakenbeck,R. and Bruckner,R. (2007)
Identification of the genes directly controlled by the response
regulator CiaR in Streptococcus pneumoniae: five out of 15 promoters
drive expression of small non-coding RNAs. Mol. Microbiol., 66,
110–126.
37. Zahner,D., Kaminski,K., van der Linden,M., Mascher,T., Meral,M.
and Hakenbeck,R. (2002) The ciaR/ciaH regulatory network of
Streptococcus pneumoniae. J. Mol. Microbiol. Biotechnol., 4, 211–216.
38. van Opijnen,T., Bodi,K.L. and Camilli,A. (2009) Tn-seq:
high-throughput parallel sequencing for fitness and genetic
interaction studies in microorganisms. Nat. Methods, 6, 767–772.
39. Dressaire,C., Picard,F., Redon,E., Loubière,P., Queinnec,I., Girbal,L.
and Cocaign-Bousquet,M. (2013) Role of mRNA stability during
bacterial adaptation. PLoS ONE, 8, e59059.
40. Dudoit,S., Yang,Y.H., Callow,M.J. and Speed,T.P. (2002) Statistical
methods for identifying differentially expressed genes in replicated
cDNA microarray experiments. Stat. Sin., 12, 111–140.
41. Bochkareva,A., Yuzenkova,Y., Tadigotla,V.R. and Zenkin,N. (2012)
Factor-independent transcription pausing caused by recognition of
the RNA-DNA hybrid sequence. EMBO J., 31, 630–639.
42. Yuzenkova,Y., Bochkareva,A., Tadigotla,V.R., Roghanian,M.,
Zorov,S., Severinov,K. and Zenkin,N. (2010) Stepwise mechanism for
transcription fidelity. BMC Biol., 8, 1–15.
43. Sidorenkov,I., Komissarova,N. and Kashlev,M. (1998) Crucial role of
the RNA:DNA hybrid in the processivity of transcription. Mol. Cell,
2, 55–64.
44. Vogel,U., Sørensen,M., Pedersen,S., Jensen,K.F. and Kilstrup,M.
(1992) Decreasing transcription elongation rate in Escherichia coli
exposed to amino acid starvation. Mol. Microbiol., 6, 2191–2200.
45. Epshtein,V. and Nudler,E. (2003) Cooperation between RNA
polymerase molecules in transcription elongation. Science, 300,
801–805.
46. Vogel,U. and Jensen,K.F. (1995) Effects of the antiterminator BoxA
on transcription elongation kinetics and ppGpp inhibition of
transcription elongation in Escherichia coli. J. Biol. Chem., 270,
18335–18340.
47. Klumpp,S. and Hwa,T. (2009) Traffic patrol in the transcription of
ribosomal RNA. RNA Biol., 6, 392–394.
48. Dutta,D., Shatalin,K., Epshtein,V., Gottesman,M.E. and Nudler,E.
(2011) Linking RNA polymerase backtracking to genome instability
in E. coli. Cell, 146, 533–543.
49. Tehranchi,A.K., Blankschien,M.D., Zhang,Y., Halliday,J.A.,
Srivatsan,A., Peng,J., Herman,C. and Wang,J.D. (2010) The
transcription factor DksA prevents conflicts between DNA
replication and transcription machinery. Cell, 141, 595–605.
50. Roberts,J.W., Shankar,S. and Filter,J.J. (2008) RNA polymerase
elongation factors. Annu. Rev. Microbiol., 62, 211–233.
51. Danko,C.G., Hah,N., Luo,X., Martins,A.L., Core,L., Lis,J.T.,
Siepel,A. and Kraus,W.L. (2013) Signaling pathways differentially
affect RNA polymerase II initiation, pausing, and elongation rate in
cells. Mol. Cell, 50, 212–222.
52. Larson,M.H., Mooney,R.A., Peters,J.M., Windgassen,T., Nayak,D.,
Gross,C.A., Block,S.M., Greenleaf,W.J., Landick,R. and
Weissman,J.S. (2014) A pause sequence enriched at translation start
sites drives transcription dynamics in vivo. Science, 344, 1042–1047.
53. O’Brien,K.L., Wolfson,L.J., Watt,J.P., Henkle,E., Deloria-Knoll,M.,
McCall,N., Lee,E., Mulholland,K., Levine,O.S. and Cherian,T.
(2009) Burden of disease caused by Streptococcus pneumoniae in
children younger than 5 years: global estimates. Lancet, 374, 893–902.
54. van der Poll,T. and Opal,S.M. (2009) Pathogenesis, treatment, and
prevention of pneumococcal pneumonia. Lancet, 374, 1543–1556.